Triple
T30709041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lindsay |
E781838
|
entity |
| Predicate | historicalGenderUsage |
P51355
|
FINISHED |
| Object | originally masculine |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: originally masculine | Statement: [Lindsay, historicalGenderUsage, originally masculine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalGenderUsage Context triple: [Lindsay, historicalGenderUsage, originally masculine]
-
A.
hasGenderedHistoricalTerm
Indicates that one entity is referred to by a historically used term whose form or usage is specific to a particular gender.
-
B.
hasAlternativeGenderUsage
Indicates that an entity is used with a different or non-standard gender form in certain contexts or usages.
-
C.
hasGenderHistory
chosen
Indicates that an entity has undergone or experienced a change or transition in gender over time.
-
D.
hasNameGenderUsage
Indicates that a particular name is used with a specific gender or set of genders in a given context.
-
E.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f224abfcf081909492e64d3cc35262 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ff109695008190a22b47ef8be2e3f9 |
completed | May 9, 2026, 10:46 a.m. |
| PD | Predicate disambiguation | batch_69ff0f243ea88190970d2c520b55c816 |
completed | May 9, 2026, 10:40 a.m. |
Created at: April 29, 2026, 8:35 p.m.